{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temporal Classification"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A logistic regression analyzes the relationship between a binary target variable and its predictor variables to estimate the probability of the dependent variable taking the value 1. In the presence of temporal data where observations along time aren't independent, the errors of the model will be correlated through time and incorporating autoregressive features or lags can capture temporal dependencies and enhance the predictive power of logistic regression.\n",
    "\n",
    "<br>NHITS's inputs are static exogenous $\\mathbf{x}^{(s)}$, historic exogenous $\\mathbf{x}^{(h)}_{[:t]}$, exogenous available at the time of the prediction $\\mathbf{x}^{(f)}_{[:t+H]}$ and autorregresive features $\\mathbf{y}_{[:t]}$, each of these inputs is further decomposed into categorical and continuous. The network uses a multi-quantile regression to model the following conditional probability:$$\\mathbb{P}(\\mathbf{y}_{[t+1:t+H]}|\\;\\mathbf{y}_{[:t]},\\; \\mathbf{x}^{(h)}_{[:t]},\\; \\mathbf{x}^{(f)}_{[:t+H]},\\; \\mathbf{x}^{(s)})$$\n",
    "\n",
    "In this notebook we show how to fit NeuralForecast methods for binary sequences regression. We will:\n",
    "- Installing NeuralForecast.\n",
    "- Loading binary sequence data.\n",
    "- Fit and predict temporal classifiers.\n",
    "- Plot and evaluate predictions.\n",
    "\n",
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Temporal_Classifiers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import MLP, NHITS, LSTM\n",
    "from neuralforecast.losses.pytorch import DistributionLoss, Accuracy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading Binary Sequence Data\n",
    "\n",
    "The `core.NeuralForecast` class contains shared, `fit`, `predict` and other methods that take as inputs pandas DataFrames with columns `['unique_id', 'ds', 'y']`, where `unique_id` identifies individual time series from the dataset, `ds` is the date, and `y` is the target binary variable. \n",
    "\n",
    "In this motivation example we convert 8x8 digits images into 64-length sequences and define a classification problem, to identify when the pixels surpass certain threshold.\n",
    "We declare a pandas dataframe in long format, to match NeuralForecast's inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "digits = datasets.load_digits()\n",
    "images = digits.images[:100]\n",
    "\n",
    "plt.imshow(images[0,:,:], cmap=plt.cm.gray, \n",
    "           vmax=16, interpolation=\"nearest\")\n",
    "\n",
    "pixels = np.reshape(images, (len(images), 64))\n",
    "ytarget = (pixels > 10) * 1\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(pixels[10])\n",
    "ax2.plot(ytarget[10], color='purple')\n",
    "ax1.set_xlabel('Pixel index')\n",
    "ax1.set_ylabel('Pixel value')\n",
    "ax2.set_ylabel('Pixel threshold', color='purple')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1910</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1912</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1913</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1914</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6395</th>\n",
       "      <td>99</td>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6396</th>\n",
       "      <td>99</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6397</th>\n",
       "      <td>99</td>\n",
       "      <td>1971</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6398</th>\n",
       "      <td>99</td>\n",
       "      <td>1972</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6399</th>\n",
       "      <td>99</td>\n",
       "      <td>1973</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6400 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id    ds  y  pixels\n",
       "0             0  1910  0     0.0\n",
       "1             0  1911  0     0.0\n",
       "2             0  1912  0     5.0\n",
       "3             0  1913  1    13.0\n",
       "4             0  1914  0     9.0\n",
       "...         ...   ... ..     ...\n",
       "6395         99  1969  1    14.0\n",
       "6396         99  1970  1    16.0\n",
       "6397         99  1971  0     3.0\n",
       "6398         99  1972  0     0.0\n",
       "6399         99  1973  0     0.0\n",
       "\n",
       "[6400 rows x 4 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We flat the images and create an input dataframe\n",
    "# with 'unique_id' series identifier and 'ds' time stamp identifier.\n",
    "Y_df = pd.DataFrame.from_dict({\n",
    "            'unique_id': np.repeat(np.arange(100), 64),\n",
    "            'ds': np.tile(np.arange(64)+1910, 100),\n",
    "            'y': ytarget.flatten(), 'pixels': pixels.flatten()})\n",
    "Y_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fit and predict temporal classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `NeuralForecast.fit` method you can train a set of models to your dataset. You can define the forecasting `horizon` (12 in this example), and modify the hyperparameters of the model. For example, for the `NHITS` we changed the default hidden size for both encoder and decoders.\n",
    "\n",
    "See the `NHITS` and `MLP` [model documentation](https://nixtla.github.io/neuralforecast/models.mlp.html).\n",
    "\n",
    ":::{.callout-warning}\n",
    "For the moment Recurrent-based model family is not available to operate with Bernoulli distribution output.\n",
    "This affects the following methods `LSTM`, `GRU`, `DilatedRNN`, and `TCN`. This feature is work in progress.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%capture\n",
    "horizon = 12\n",
    "\n",
    "# Try different hyperparmeters to improve accuracy.\n",
    "models = [MLP(h=horizon,                           # Forecast horizon\n",
    "              input_size=2 * horizon,              # Length of input sequence\n",
    "              loss=DistributionLoss('Bernoulli'),  # Binary classification loss\n",
    "              valid_loss=Accuracy(),               # Accuracy validation signal\n",
    "              max_steps=500,                       # Number of steps to train\n",
    "              scaler_type='standard',              # Type of scaler to normalize data\n",
    "              hidden_size=64,                      # Defines the size of the hidden state of the LSTM\n",
    "              #early_stop_patience_steps=2,         # Early stopping regularization patience\n",
    "              val_check_steps=10,                  # Frequency of validation signal (affects early stopping)\n",
    "              ),\n",
    "          NHITS(h=horizon,                          # Forecast horizon\n",
    "                input_size=2 * horizon,             # Length of input sequence\n",
    "                loss=DistributionLoss('Bernoulli'), # Binary classification loss\n",
    "                valid_loss=Accuracy(),              # Accuracy validation signal                \n",
    "                max_steps=500,                      # Number of steps to train\n",
    "                n_freq_downsample=[2, 1, 1],        # Downsampling factors for each stack output\n",
    "                #early_stop_patience_steps=2,        # Early stopping regularization patience\n",
    "                val_check_steps=10,                 # Frequency of validation signal (affects early stopping)\n",
    "                interpolation_mode=\"nearest\",\n",
    "                )             \n",
    "          ]\n",
    "nf = NeuralForecast(models=models, freq=1)\n",
    "Y_hat_df = nf.cross_validation(df=Y_df, n_windows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>MLP</th>\n",
       "      <th>MLP-median</th>\n",
       "      <th>MLP-lo-90</th>\n",
       "      <th>MLP-lo-80</th>\n",
       "      <th>MLP-hi-80</th>\n",
       "      <th>MLP-hi-90</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>NHITS-median</th>\n",
       "      <th>NHITS-lo-90</th>\n",
       "      <th>NHITS-lo-80</th>\n",
       "      <th>NHITS-hi-80</th>\n",
       "      <th>NHITS-hi-90</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1962</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.761</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.784</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1964</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1965</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>99</td>\n",
       "      <td>1969</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.551</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>99</td>\n",
       "      <td>1970</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.662</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>99</td>\n",
       "      <td>1971</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>99</td>\n",
       "      <td>1972</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>99</td>\n",
       "      <td>1973</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id    ds  cutoff    MLP  MLP-median  MLP-lo-90  MLP-lo-80  \\\n",
       "0             0  1962    1961  0.173         0.0        0.0        0.0   \n",
       "1             0  1963    1961  0.784         1.0        0.0        0.0   \n",
       "2             0  1964    1961  0.042         0.0        0.0        0.0   \n",
       "3             0  1965    1961  0.072         0.0        0.0        0.0   \n",
       "4             0  1966    1961  0.059         0.0        0.0        0.0   \n",
       "...         ...   ...     ...    ...         ...        ...        ...   \n",
       "1195         99  1969    1961  0.551         1.0        0.0        0.0   \n",
       "1196         99  1970    1961  0.662         1.0        0.0        0.0   \n",
       "1197         99  1971    1961  0.369         0.0        0.0        0.0   \n",
       "1198         99  1972    1961  0.056         0.0        0.0        0.0   \n",
       "1199         99  1973    1961  0.000         0.0        0.0        0.0   \n",
       "\n",
       "      MLP-hi-80  MLP-hi-90  NHITS  NHITS-median  NHITS-lo-90  NHITS-lo-80  \\\n",
       "0           1.0        1.0  0.761           1.0          0.0          0.0   \n",
       "1           1.0        1.0  0.571           1.0          0.0          0.0   \n",
       "2           0.0        0.0  0.009           0.0          0.0          0.0   \n",
       "3           0.0        1.0  0.054           0.0          0.0          0.0   \n",
       "4           0.0        1.0  0.000           0.0          0.0          0.0   \n",
       "...         ...        ...    ...           ...          ...          ...   \n",
       "1195        1.0        1.0  0.697           1.0          0.0          0.0   \n",
       "1196        1.0        1.0  0.465           0.0          0.0          0.0   \n",
       "1197        1.0        1.0  0.382           0.0          0.0          0.0   \n",
       "1198        0.0        1.0  0.000           0.0          0.0          0.0   \n",
       "1199        0.0        0.0  0.000           0.0          0.0          0.0   \n",
       "\n",
       "      NHITS-hi-80  NHITS-hi-90  y  \n",
       "0             1.0          1.0  0  \n",
       "1             1.0          1.0  1  \n",
       "2             0.0          0.0  0  \n",
       "3             0.0          1.0  0  \n",
       "4             0.0          0.0  0  \n",
       "...           ...          ... ..  \n",
       "1195          1.0          1.0  1  \n",
       "1196          1.0          1.0  1  \n",
       "1197          1.0          1.0  0  \n",
       "1198          0.0          0.0  0  \n",
       "1199          0.0          0.0  0  \n",
       "\n",
       "[1200 rows x 16 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By default NeuralForecast produces forecast intervals\n",
    "# In this case the lo-x and high-x levels represent the \n",
    "# low and high bounds of the prediction accumulating x% probability\n",
    "Y_hat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>99</td>\n",
       "      <td>1973</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id    ds  cutoff  MLP  MLP-median  MLP-lo-90  MLP-lo-80  \\\n",
       "0             0  1962    1961    0         0.0        0.0        0.0   \n",
       "1             0  1963    1961    1         1.0        0.0        0.0   \n",
       "2             0  1964    1961    0         0.0        0.0        0.0   \n",
       "3             0  1965    1961    0         0.0        0.0        0.0   \n",
       "4             0  1966    1961    0         0.0        0.0        0.0   \n",
       "...         ...   ...     ...  ...         ...        ...        ...   \n",
       "1195         99  1969    1961    1         1.0        0.0        0.0   \n",
       "1196         99  1970    1961    1         1.0        0.0        0.0   \n",
       "1197         99  1971    1961    0         0.0        0.0        0.0   \n",
       "1198         99  1972    1961    0         0.0        0.0        0.0   \n",
       "1199         99  1973    1961    0         0.0        0.0        0.0   \n",
       "\n",
       "      MLP-hi-80  MLP-hi-90  NHITS  NHITS-median  NHITS-lo-90  NHITS-lo-80  \\\n",
       "0           1.0        1.0      1           1.0          0.0          0.0   \n",
       "1           1.0        1.0      1           1.0          0.0          0.0   \n",
       "2           0.0        0.0      0           0.0          0.0          0.0   \n",
       "3           0.0        1.0      0           0.0          0.0          0.0   \n",
       "4           0.0        1.0      0           0.0          0.0          0.0   \n",
       "...         ...        ...    ...           ...          ...          ...   \n",
       "1195        1.0        1.0      1           1.0          0.0          0.0   \n",
       "1196        1.0        1.0      0           0.0          0.0          0.0   \n",
       "1197        1.0        1.0      0           0.0          0.0          0.0   \n",
       "1198        0.0        1.0      0           0.0          0.0          0.0   \n",
       "1199        0.0        0.0      0           0.0          0.0          0.0   \n",
       "\n",
       "      NHITS-hi-80  NHITS-hi-90  y  \n",
       "0             1.0          1.0  0  \n",
       "1             1.0          1.0  1  \n",
       "2             0.0          0.0  0  \n",
       "3             0.0          1.0  0  \n",
       "4             0.0          0.0  0  \n",
       "...           ...          ... ..  \n",
       "1195          1.0          1.0  1  \n",
       "1196          1.0          1.0  1  \n",
       "1197          1.0          1.0  0  \n",
       "1198          0.0          0.0  0  \n",
       "1199          0.0          0.0  0  \n",
       "\n",
       "[1200 rows x 16 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define classification threshold for final predictions\n",
    "# If (prob > threshold) -> 1\n",
    "Y_hat_df['NHITS'] = (Y_hat_df['NHITS'] > 0.5) * 1\n",
    "Y_hat_df['MLP'] = (Y_hat_df['MLP'] > 0.5) * 1\n",
    "Y_hat_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot and Evaluate Predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the forecasts of both models againts the real values.\n",
    "And evaluate the accuracy of the `MLP` and `NHITS` temporal classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = Y_hat_df[Y_hat_df.unique_id==10]\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
    "plt.plot(plot_df.ds, plot_df.y, label='target signal')\n",
    "plt.plot(plot_df.ds, plot_df['MLP'] * 1.1, label='MLP prediction')\n",
    "plt.plot(plot_df.ds, plot_df['NHITS'] * .9, label='NHITS prediction')\n",
    "ax.set_title('Binary Sequence Forecast', fontsize=22)\n",
    "ax.set_ylabel('Pixel Threshold and Prediction', fontsize=20)\n",
    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
    "ax.legend(prop={'size': 15})\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP Accuracy: 77.8%\n",
      "NHITS Accuracy: 74.5%\n"
     ]
    }
   ],
   "source": [
    "def accuracy(y, y_hat):\n",
    "    return np.mean(y==y_hat)\n",
    "\n",
    "mlp_acc = accuracy(y=Y_hat_df['y'], y_hat=Y_hat_df['MLP'])\n",
    "nhits_acc = accuracy(y=Y_hat_df['y'], y_hat=Y_hat_df['NHITS'])\n",
    "\n",
    "print(f'MLP Accuracy: {mlp_acc:.1%}')\n",
    "print(f'NHITS Accuracy: {nhits_acc:.1%}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- [Cox D. R. (1958). “The Regression Analysis of Binary Sequences.” Journal of the Royal Statistical Society B, 20(2), 215–242.](https://arxiv.org/abs/2201.12886)\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
